This document provides an introduction and overview of reinforcement learning. It discusses key concepts like the reinforcement learning problem formulation involving an agent and environment, rewards, values, and policies. It also covers core reinforcement learning concepts like prediction, control, learning, and planning. Example problems are presented for Atari games, mazes, and gridworlds to illustrate different reinforcement learning techniques. The course will focus on understanding fundamental principles and algorithms for learning through interaction, covering topics such as exploration, planning, model-free and model-based methods, and deep reinforcement learning.
This document provides an overview of an introductory lecture on reinforcement learning. The key points covered include:
- Reinforcement learning involves an agent learning through trial-and-error interactions with an environment by receiving rewards.
- The goal of reinforcement learning is for the agent to select actions that maximize total rewards. This involves making decisions to balance short-term versus long-term rewards.
- Major components of a reinforcement learning agent include its policy, which determines its behavior, its value function which predicts future rewards, and its model which represents its understanding of the environment's dynamics.
This document discusses reinforcement learning, an approach to machine learning where an agent learns behaviors through trial and error interactions with its environment. The agent receives positive or negative feedback based on its actions, allowing it to maximize rewards. Specifically:
1) In reinforcement learning, an agent performs actions in an environment and receives feedback in the form of rewards or punishments to learn behaviors without a teacher directly telling it what to do.
2) The goal is for the agent to learn a policy to map states to actions that will maximize total rewards. It must figure out which of its past actions led to rewards through the "credit assignment problem."
3) Reinforcement learning has been applied to problems like game playing, robot control
The document discusses reinforcement learning. It defines reinforcement learning as learning via interactions with an environment where an agent receives rewards or penalties for its actions without being told which actions are correct. The document outlines different types of learning including supervised learning and reinforcement learning. It also discusses key concepts in reinforcement learning including the reinforcement learning model, model-based vs model-free approaches, passive vs active learning, exploration problems, and using generalization techniques like function approximation to deal with large state spaces.
The document discusses reinforcement learning. It defines reinforcement learning as learning via interactions with an environment where an agent receives rewards or penalties for its actions without being told which actions are correct. The document outlines different types of learning including supervised learning and reinforcement learning. It also discusses key concepts in reinforcement learning including the reinforcement learning model, model-based vs model-free approaches, passive vs active learning, exploration problems, and using generalization techniques like function approximation to deal with large state spaces.
Reinforcement learning involves an agent learning how to behave through trial-and-error interactions with an environment. The agent receives rewards or punishments without being told the correct actions. There are two main types of learning - supervised learning where examples are provided, and reinforcement learning where only evaluations are provided. Reinforcement learning can be modeled as a Markov decision process and approached through model-based methods which learn the environment model, or model-free methods like temporal difference learning which learn directly from experiences. Active learning requires an agent to consider the impact of actions on both immediate and long-term rewards. Exploration strategies balance exploiting current knowledge with exploring unknown areas. Generalization techniques like function approximation can help scale reinforcement learning to large problems.
Reinforcement learning involves an agent learning how to behave through trial-and-error interactions with an environment. The agent receives rewards or punishments that influence the agent's actions without being explicitly told which actions to take. There are two main approaches: model-based learns a model of the environment and uses it to derive an optimal policy, while model-free derives a policy without learning the environment model. Exploration vs exploitation tradeoff involves balancing gaining rewards with exploring to improve long-term learning. Methods like Q-learning and genetic algorithms are used to generalize to large state/action spaces.
This document provides an introduction to machine learning and intelligent agents. It defines machine learning as programs that automatically improve performance through experience without human assistance. Examples of machine learning problems include optical character recognition, spam filtering, and medical diagnosis. Learning models define the learning problem formally. Intelligent agents can perceive their environment and act upon it. Rational agents aim to perform useful actions. The document discusses properties of environments including observable vs partially observable. It outlines different types of agents from simple reflex agents to goal-based and utility-based agents.
This document provides an overview of an introductory lecture on reinforcement learning. The key points covered include:
- Reinforcement learning involves an agent learning through trial-and-error interactions with an environment by receiving rewards.
- The goal of reinforcement learning is for the agent to select actions that maximize total rewards. This involves making decisions to balance short-term versus long-term rewards.
- Major components of a reinforcement learning agent include its policy, which determines its behavior, its value function which predicts future rewards, and its model which represents its understanding of the environment's dynamics.
This document discusses reinforcement learning, an approach to machine learning where an agent learns behaviors through trial and error interactions with its environment. The agent receives positive or negative feedback based on its actions, allowing it to maximize rewards. Specifically:
1) In reinforcement learning, an agent performs actions in an environment and receives feedback in the form of rewards or punishments to learn behaviors without a teacher directly telling it what to do.
2) The goal is for the agent to learn a policy to map states to actions that will maximize total rewards. It must figure out which of its past actions led to rewards through the "credit assignment problem."
3) Reinforcement learning has been applied to problems like game playing, robot control
The document discusses reinforcement learning. It defines reinforcement learning as learning via interactions with an environment where an agent receives rewards or penalties for its actions without being told which actions are correct. The document outlines different types of learning including supervised learning and reinforcement learning. It also discusses key concepts in reinforcement learning including the reinforcement learning model, model-based vs model-free approaches, passive vs active learning, exploration problems, and using generalization techniques like function approximation to deal with large state spaces.
The document discusses reinforcement learning. It defines reinforcement learning as learning via interactions with an environment where an agent receives rewards or penalties for its actions without being told which actions are correct. The document outlines different types of learning including supervised learning and reinforcement learning. It also discusses key concepts in reinforcement learning including the reinforcement learning model, model-based vs model-free approaches, passive vs active learning, exploration problems, and using generalization techniques like function approximation to deal with large state spaces.
Reinforcement learning involves an agent learning how to behave through trial-and-error interactions with an environment. The agent receives rewards or punishments without being told the correct actions. There are two main types of learning - supervised learning where examples are provided, and reinforcement learning where only evaluations are provided. Reinforcement learning can be modeled as a Markov decision process and approached through model-based methods which learn the environment model, or model-free methods like temporal difference learning which learn directly from experiences. Active learning requires an agent to consider the impact of actions on both immediate and long-term rewards. Exploration strategies balance exploiting current knowledge with exploring unknown areas. Generalization techniques like function approximation can help scale reinforcement learning to large problems.
Reinforcement learning involves an agent learning how to behave through trial-and-error interactions with an environment. The agent receives rewards or punishments that influence the agent's actions without being explicitly told which actions to take. There are two main approaches: model-based learns a model of the environment and uses it to derive an optimal policy, while model-free derives a policy without learning the environment model. Exploration vs exploitation tradeoff involves balancing gaining rewards with exploring to improve long-term learning. Methods like Q-learning and genetic algorithms are used to generalize to large state/action spaces.
This document provides an introduction to machine learning and intelligent agents. It defines machine learning as programs that automatically improve performance through experience without human assistance. Examples of machine learning problems include optical character recognition, spam filtering, and medical diagnosis. Learning models define the learning problem formally. Intelligent agents can perceive their environment and act upon it. Rational agents aim to perform useful actions. The document discusses properties of environments including observable vs partially observable. It outlines different types of agents from simple reflex agents to goal-based and utility-based agents.
An efficient use of temporal difference technique in Computer Game LearningPrabhu Kumar
This document summarizes an efficient use of temporal difference techniques in computer game learning. It discusses reinforcement learning and some key concepts including the agent-environment interface, types of reinforcement learning tasks, elements of reinforcement learning like policy, reward functions, and value functions. It also describes algorithms like dynamic programming, policy iteration, value iteration, and temporal difference learning. Finally, it mentions some applications of reinforcement learning in benchmark problems, games, and real-world domains like robotics and control.
An agent interacts with an environment to maximize rewards. Reinforcement learning algorithms learn through trial and error by taking actions and receiving rewards or penalties. The document discusses reinforcement learning concepts like the agent, environment, actions, policy, and rewards. It also summarizes OpenAI Gym, a toolkit for developing and comparing reinforcement learning algorithms with different environments like CartPole. Code examples are provided to interact with environments using a hardcoded policy and a basic neural network.
This document summarizes machine learning and inductive logic programming techniques for multi-agent systems. It discusses using machine learning for single agents and multi-agent systems, including inductive learning, reinforcement learning, and unsupervised learning. For multi-agent systems, it covers social awareness, communication, and role learning using techniques like Q-learning.
The document summarizes key concepts in reinforcement learning:
- Agent-environment interaction is modeled as states, actions, and rewards
- A policy is a rule for selecting actions in each state
- The return is the total discounted future reward an agent aims to maximize
- Tasks can be episodic or continuing
- The Markov property means the future depends only on the present state
- The agent-environment framework can be modeled as a Markov decision process
This is the material of a 4 hour class given in the framework of a EES-UETP class (Electrical Energy Systems - University Enterprise Training Partnership). The first part gives a brief overview of the applications of reinforcement learning for solving decision-making problems related to electrical systems. The second part explains how to build intelligent agents using reinforcement learning.
The document is a seminar report submitted by Kalaissiram S. for their Bachelor of Technology degree. It discusses reinforcement learning (RL), including the key concepts of agents, environments, actions, states, rewards, and policies. It also covers the Bellman equation, types of RL, Markov decision processes, popular RL algorithms like Q-learning and SARSA, and applications of RL.
RL_Dr.SNR Final ppt for Presentation 28.05.2021.pptxdeeplearning6
This document provides an overview of reinforcement learning. It discusses key concepts like rewards, environment, state, and the reinforcement learning agent. The agent learns through trial-and-error interactions with its environment by trying different actions and receiving feedback in the form of rewards. The goal is to learn a policy that maximizes long-term rewards by balancing exploration of new actions with exploitation of known rewarding actions. The document also covers reinforcement learning problems, components of the reinforcement learning agent, and different categories of reinforcement learning methods.
This document discusses different types of intelligent agents. It defines an agent as an entity that perceives its environment and acts upon that environment. Rational agents are defined as those that select actions that maximize their performance given the information available. Six main types of agents are described: (1) table-driven agents that use lookup tables; (2) simple reflex agents that act solely based on current percepts; (3) model-based reflex agents that track past states; (4) goal-based agents that consider future actions to achieve goals; (5) utility-based agents that make decisions based on utility theory; and (6) learning agents that improve through experience. The document emphasizes that representing knowledge is important for successful agent design
Reinforcement learning is a computational approach for learning through interaction without an explicit teacher. An agent takes actions in various states and receives rewards, allowing it to learn relationships between situations and optimal actions. The goal is to learn a policy that maximizes long-term rewards by balancing exploitation of current knowledge with exploration of new actions. Methods like Q-learning use value function approximation and experience replay in deep neural networks to scale to complex problems with large state spaces like video games. Temporal difference learning combines the advantages of Monte Carlo and dynamic programming by bootstrapping values from current estimates rather than waiting for full episodes.
Reinforcement learning is a machine learning technique that involves an agent learning how to achieve a goal in an environment by trial-and-error using feedback in the form of rewards and punishments. The agent learns an optimal behavior or policy for achieving the maximum reward. Key elements of reinforcement learning include the agent, environment, states, actions, policy, reward function, and value function. Reinforcement learning problems can be solved using methods like dynamic programming, Monte Carlo methods, and temporal difference learning.
1. Reinforcement learning involves an agent learning through trial-and-error interactions with an environment. The agent learns a policy for how to act by maximizing rewards.
2. The document outlines key elements of reinforcement learning including states, actions, rewards, value functions, and explores different methods for solving reinforcement learning problems including dynamic programming, Monte Carlo methods, and temporal difference learning.
3. Temporal difference learning combines the advantages of Monte Carlo methods and dynamic programming by allowing for incremental learning through bootstrapping predictions like dynamic programming while also learning directly from experience like Monte Carlo methods.
An agent is anything that perceives its environment through sensors and acts upon the environment through actuators. Intelligent agents can be human, robots with cameras and motors, or thermostats detecting room temperature. An agent's behavior is described by its agent function, which maps percept sequences to actions. Rational agents select actions expected to maximize a performance measure given the agent's knowledge and percept sequence. Learning agents can improve their performance over time by experiencing examples.
Designing an AI that gains experience for absolute beginnersTanzim Saqib
1) The document discusses reinforcement learning (RL), which involves an agent learning from interactions with an environment by trial-and-error using rewards and punishments.
2) Key aspects of RL include states, actions, rewards, and the goal of maximizing cumulative reward over time. The agent's objective is to learn policies for making optimal decisions.
3) RL problems can be modeled as Markov decision processes and solved using techniques like dynamic programming and the Bellman equation to estimate state values. RL agents use rewards to develop long-term memories to guide future decision making.
An intelligent agent is anything that can perceive its environment through sensors and act upon that environment through effectors. Intelligent agents include humans, robots, and thermostats. An agent's behavior is determined by its agent function, which maps percept sequences to actions. Rational agents are those that maximize their performance as defined by a performance measure. Agent programs implement agent functions in a way that uses minimal code rather than exhaustive lookup tables. There are different types of agent programs including simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents.
The document discusses artificial intelligence (AI) and intelligent agents. It covers four categories of views on AI, the foundations of AI including philosophy, mathematics, psychology and computer science. It also discusses early pioneers like Turing who proposed the Turing test and anticipated arguments against AI. The document outlines requirements for validating theories of intelligence and distinguishes cognitive science from AI. It defines rational behavior and intelligent agents, and discusses designing rational agents to perform well according to a performance measure in any given environment.
Reinforcement learning algorithms like Q-learning, SARSA, DQN, and A3C help agents learn optimal behaviors through trial-and-error interactions with an environment. Q-learning uses a model-free approach to estimate state-action values without a transition model. SARSA is similar to Q-learning but is on-policy, learning the value function from the current policy. DQN approximates Q-values using a neural network to handle large state spaces. A3C uses multiple asynchronous agents interacting with individual environments to learn diversified policies through an actor-critic framework.
This document discusses reinforcement learning, which is a machine learning method where an agent learns behavior through trial-and-error interactions with a dynamic environment. The agent receives rewards or punishments that guide its learning of a policy to maximize rewards. Key elements of reinforcement learning include the agent, environment, policy, reward function, and value function. The learning process involves the agent observing a state, choosing an action based on its policy, receiving a reward, and updating its knowledge to improve future actions. Reinforcement learning emphasizes learning from feedback without being explicitly told the correct actions.
This document provides an overview of deep reinforcement learning. It begins with an introduction to reinforcement learning and discusses key concepts like agents, environments, states, actions, rewards, and policies. It then covers important reinforcement learning algorithms like Q-learning and Deep Q-Networks. The document provides examples of deep reinforcement learning being applied to problems like playing Atari games and robotics. It concludes with discussing tools and libraries used for deep reinforcement learning.
This document discusses reinforcement learning. It defines reinforcement learning as a learning method where an agent learns how to behave via interactions with an environment. The agent receives rewards or penalties based on its actions but is not told which actions are correct. Several reinforcement learning concepts and algorithms are covered, including model-based vs model-free approaches, passive vs active learning, temporal difference learning, adaptive dynamic programming, and exploration-exploitation tradeoffs. Generalization methods like function approximation and genetic algorithms are also briefly mentioned.
This document provides an overview of reinforcement learning and some key algorithms used in artificial intelligence. It introduces reinforcement learning concepts like Markov decision processes, value functions, temporal difference learning methods like Q-learning and SARSA, and policy gradient methods. It also describes deep reinforcement learning techniques like deep Q-networks that combine reinforcement learning with deep neural networks. Deep Q-networks use experience replay and fixed length state representations to allow deep neural networks to approximate the Q-function and learn successful policies from high dimensional input like images.
An efficient use of temporal difference technique in Computer Game LearningPrabhu Kumar
This document summarizes an efficient use of temporal difference techniques in computer game learning. It discusses reinforcement learning and some key concepts including the agent-environment interface, types of reinforcement learning tasks, elements of reinforcement learning like policy, reward functions, and value functions. It also describes algorithms like dynamic programming, policy iteration, value iteration, and temporal difference learning. Finally, it mentions some applications of reinforcement learning in benchmark problems, games, and real-world domains like robotics and control.
An agent interacts with an environment to maximize rewards. Reinforcement learning algorithms learn through trial and error by taking actions and receiving rewards or penalties. The document discusses reinforcement learning concepts like the agent, environment, actions, policy, and rewards. It also summarizes OpenAI Gym, a toolkit for developing and comparing reinforcement learning algorithms with different environments like CartPole. Code examples are provided to interact with environments using a hardcoded policy and a basic neural network.
This document summarizes machine learning and inductive logic programming techniques for multi-agent systems. It discusses using machine learning for single agents and multi-agent systems, including inductive learning, reinforcement learning, and unsupervised learning. For multi-agent systems, it covers social awareness, communication, and role learning using techniques like Q-learning.
The document summarizes key concepts in reinforcement learning:
- Agent-environment interaction is modeled as states, actions, and rewards
- A policy is a rule for selecting actions in each state
- The return is the total discounted future reward an agent aims to maximize
- Tasks can be episodic or continuing
- The Markov property means the future depends only on the present state
- The agent-environment framework can be modeled as a Markov decision process
This is the material of a 4 hour class given in the framework of a EES-UETP class (Electrical Energy Systems - University Enterprise Training Partnership). The first part gives a brief overview of the applications of reinforcement learning for solving decision-making problems related to electrical systems. The second part explains how to build intelligent agents using reinforcement learning.
The document is a seminar report submitted by Kalaissiram S. for their Bachelor of Technology degree. It discusses reinforcement learning (RL), including the key concepts of agents, environments, actions, states, rewards, and policies. It also covers the Bellman equation, types of RL, Markov decision processes, popular RL algorithms like Q-learning and SARSA, and applications of RL.
RL_Dr.SNR Final ppt for Presentation 28.05.2021.pptxdeeplearning6
This document provides an overview of reinforcement learning. It discusses key concepts like rewards, environment, state, and the reinforcement learning agent. The agent learns through trial-and-error interactions with its environment by trying different actions and receiving feedback in the form of rewards. The goal is to learn a policy that maximizes long-term rewards by balancing exploration of new actions with exploitation of known rewarding actions. The document also covers reinforcement learning problems, components of the reinforcement learning agent, and different categories of reinforcement learning methods.
This document discusses different types of intelligent agents. It defines an agent as an entity that perceives its environment and acts upon that environment. Rational agents are defined as those that select actions that maximize their performance given the information available. Six main types of agents are described: (1) table-driven agents that use lookup tables; (2) simple reflex agents that act solely based on current percepts; (3) model-based reflex agents that track past states; (4) goal-based agents that consider future actions to achieve goals; (5) utility-based agents that make decisions based on utility theory; and (6) learning agents that improve through experience. The document emphasizes that representing knowledge is important for successful agent design
Reinforcement learning is a computational approach for learning through interaction without an explicit teacher. An agent takes actions in various states and receives rewards, allowing it to learn relationships between situations and optimal actions. The goal is to learn a policy that maximizes long-term rewards by balancing exploitation of current knowledge with exploration of new actions. Methods like Q-learning use value function approximation and experience replay in deep neural networks to scale to complex problems with large state spaces like video games. Temporal difference learning combines the advantages of Monte Carlo and dynamic programming by bootstrapping values from current estimates rather than waiting for full episodes.
Reinforcement learning is a machine learning technique that involves an agent learning how to achieve a goal in an environment by trial-and-error using feedback in the form of rewards and punishments. The agent learns an optimal behavior or policy for achieving the maximum reward. Key elements of reinforcement learning include the agent, environment, states, actions, policy, reward function, and value function. Reinforcement learning problems can be solved using methods like dynamic programming, Monte Carlo methods, and temporal difference learning.
1. Reinforcement learning involves an agent learning through trial-and-error interactions with an environment. The agent learns a policy for how to act by maximizing rewards.
2. The document outlines key elements of reinforcement learning including states, actions, rewards, value functions, and explores different methods for solving reinforcement learning problems including dynamic programming, Monte Carlo methods, and temporal difference learning.
3. Temporal difference learning combines the advantages of Monte Carlo methods and dynamic programming by allowing for incremental learning through bootstrapping predictions like dynamic programming while also learning directly from experience like Monte Carlo methods.
An agent is anything that perceives its environment through sensors and acts upon the environment through actuators. Intelligent agents can be human, robots with cameras and motors, or thermostats detecting room temperature. An agent's behavior is described by its agent function, which maps percept sequences to actions. Rational agents select actions expected to maximize a performance measure given the agent's knowledge and percept sequence. Learning agents can improve their performance over time by experiencing examples.
Designing an AI that gains experience for absolute beginnersTanzim Saqib
1) The document discusses reinforcement learning (RL), which involves an agent learning from interactions with an environment by trial-and-error using rewards and punishments.
2) Key aspects of RL include states, actions, rewards, and the goal of maximizing cumulative reward over time. The agent's objective is to learn policies for making optimal decisions.
3) RL problems can be modeled as Markov decision processes and solved using techniques like dynamic programming and the Bellman equation to estimate state values. RL agents use rewards to develop long-term memories to guide future decision making.
An intelligent agent is anything that can perceive its environment through sensors and act upon that environment through effectors. Intelligent agents include humans, robots, and thermostats. An agent's behavior is determined by its agent function, which maps percept sequences to actions. Rational agents are those that maximize their performance as defined by a performance measure. Agent programs implement agent functions in a way that uses minimal code rather than exhaustive lookup tables. There are different types of agent programs including simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents.
The document discusses artificial intelligence (AI) and intelligent agents. It covers four categories of views on AI, the foundations of AI including philosophy, mathematics, psychology and computer science. It also discusses early pioneers like Turing who proposed the Turing test and anticipated arguments against AI. The document outlines requirements for validating theories of intelligence and distinguishes cognitive science from AI. It defines rational behavior and intelligent agents, and discusses designing rational agents to perform well according to a performance measure in any given environment.
Reinforcement learning algorithms like Q-learning, SARSA, DQN, and A3C help agents learn optimal behaviors through trial-and-error interactions with an environment. Q-learning uses a model-free approach to estimate state-action values without a transition model. SARSA is similar to Q-learning but is on-policy, learning the value function from the current policy. DQN approximates Q-values using a neural network to handle large state spaces. A3C uses multiple asynchronous agents interacting with individual environments to learn diversified policies through an actor-critic framework.
This document discusses reinforcement learning, which is a machine learning method where an agent learns behavior through trial-and-error interactions with a dynamic environment. The agent receives rewards or punishments that guide its learning of a policy to maximize rewards. Key elements of reinforcement learning include the agent, environment, policy, reward function, and value function. The learning process involves the agent observing a state, choosing an action based on its policy, receiving a reward, and updating its knowledge to improve future actions. Reinforcement learning emphasizes learning from feedback without being explicitly told the correct actions.
This document provides an overview of deep reinforcement learning. It begins with an introduction to reinforcement learning and discusses key concepts like agents, environments, states, actions, rewards, and policies. It then covers important reinforcement learning algorithms like Q-learning and Deep Q-Networks. The document provides examples of deep reinforcement learning being applied to problems like playing Atari games and robotics. It concludes with discussing tools and libraries used for deep reinforcement learning.
This document discusses reinforcement learning. It defines reinforcement learning as a learning method where an agent learns how to behave via interactions with an environment. The agent receives rewards or penalties based on its actions but is not told which actions are correct. Several reinforcement learning concepts and algorithms are covered, including model-based vs model-free approaches, passive vs active learning, temporal difference learning, adaptive dynamic programming, and exploration-exploitation tradeoffs. Generalization methods like function approximation and genetic algorithms are also briefly mentioned.
This document provides an overview of reinforcement learning and some key algorithms used in artificial intelligence. It introduces reinforcement learning concepts like Markov decision processes, value functions, temporal difference learning methods like Q-learning and SARSA, and policy gradient methods. It also describes deep reinforcement learning techniques like deep Q-networks that combine reinforcement learning with deep neural networks. Deep Q-networks use experience replay and fixed length state representations to allow deep neural networks to approximate the Q-function and learn successful policies from high dimensional input like images.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
This study Examines the Effectiveness of Talent Procurement through the Imple...DharmaBanothu
In the world with high technology and fast
forward mindset recruiters are walking/showing interest
towards E-Recruitment. Present most of the HRs of
many companies are choosing E-Recruitment as the best
choice for recruitment. E-Recruitment is being done
through many online platforms like Linkedin, Naukri,
Instagram , Facebook etc. Now with high technology E-
Recruitment has gone through next level by using
Artificial Intelligence too.
Key Words : Talent Management, Talent Acquisition , E-
Recruitment , Artificial Intelligence Introduction
Effectiveness of Talent Acquisition through E-
Recruitment in this topic we will discuss about 4important
and interlinked topics which are
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
8. Motivation
I First, automation of repeated physical solutions
I Industrial revolution (1750 - 1850) and Machine Age (1870 - 1940)
I Second, automation of repeated mental solutions
I Digital revolution (1950 - now) and Information Age
I Next step: allow machines to find solutions themselves
I Artificial Intelligence
I We then only needs to specify a problem and/or goal
I This requires learning autonomously how to make decisions
10. In the process of trying to imitate an adult human mind we are bound to think a good deal about
the process which has brought it to the state that it is in. We may notice three components,
a. The initial state of the mind, say at birth,
b. The education to which it has been subjected,
c. Other experience, not to be described as education, to which it has been subjected.
Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce
one which simulates the child’s? If this were then subjected to an appropriate course of education one
would obtain the adult brain. Presumably the child-brain is something like a note-book as one buys
it from the stationers. Rather little mechanism, and lots of blank sheets. (Mechanism and writing
are from our point of view almost synonymous.) Our hope is that there is so little mechanism in
the child-brain that something like it can be easily programmed.
– Alan Turing, 1950
11. What is artificial intelligence?
I We will use the following definition of intelligence:
To be able to learn to make decisions to achieve goals
I Learning, decisions, and goals are all central
13. What is reinforcement learning?
I People and animals learn by interacting with our environment
I This differs from certain other types of learning
I It is active rather than passive
I Interactions are often sequential — future interactions can depend on earlier ones
I We are goal-directed
I We can learn without examples of optimal behaviour
I Instead, we optimise some reward signal
15. The reward hypothesis
Reinforcement learning is based on the reward hypothesis:
Any goal can be formalized as the outcome of maximizing a cumulative reward
16. Examples of RL problems
I Fly a helicopter
I Manage an investment portfolio
I Control a power station
I Make a robot walk
I Play video or board games
→ Reward: air time, inverse distance, ...
→ Reward: gains, gains minus risk, ...
→ Reward: efficiency, ...
→ Reward: distance, speed, ...
→ Reward: win, maximise score, ...
If the goal is to learn via interaction, these are all reinforcement learning problems
(Irrespective of which solution you use)
17. What is reinforcement learning?
There are distinct reasons to learn:
1. Find solutions
I A program that plays chess really well
I A manufacturing robot with a specific purpose
2. Adapt online, deal with unforeseen circumstances
I A chess program that can learn to adapt to you
I A robot that can learn to navigate unknown terrains
I Reinforcement learning can provide algorithms for both cases
I Note that the second point is not (just) about generalization — it is about continuing to
learn efficiently online, during operation
18. What is reinforcement learning?
I Science and framework of learning to make decisions from interaction
I This requires us to think about
I ...time
I ...(long-term) consequences of actions
I ...actively gathering experience
I ...predicting the future
I ...dealing with uncertainty
I Huge potential scope
I A formalisation of the AI problem
21. Agent and Environment
I At each step t the agent:
I Receives observation Ot (and reward Rt)
I Executes action At
I The environment:
I Receives action At
I Emits observation Ot+1 (and reward Rt+1)
22. Rewards
I A reward Rt is a scalar feedback signal
I Indicates how well agent is doing at step t — defines the goal
I The agent’s job is to maximize cumulative reward
Gt = Rt+1 + Rt+2 + Rt+3 + ...
I We call this the return
Reinforcement learning is based on the reward hypothesis:
Any goal can be formalized as the outcome of maximizing a cumulative reward
23. Values
I We call the expected cumulative reward, from a state s, the value
v(s) = E [Gt | St = s]
= E [Rt+1 + Rt+2 + Rt+3 + ... | St = s]
I The value depends on the actions the agent takes
I Goal is to maximize value, by picking suitable actions
I Rewards and values define utility of states and action (no supervised feedback)
I Returns and values can be defined recursively
Gt = Rt+1 + Gt+1
v(s) = E [Rt+1 + v(St+1) | St = s]
24. Maximising value by taking actions
I Goal: select actions to maximise value
I Actions may have long term consequences
I Reward may be delayed
I It may be better to sacrifice immediate reward to gain more long-term reward
I Examples:
I Refueling a helicopter (might prevent a crash in several hours)
I Defensive moves in a game (may help chances of winning later)
I Learning a new skill (can be costly & time-consuming at first)
I A mapping from states to actions is called a policy
25. Action values
I It is also possible to condition the value on actions:
q(s, a) = E [Gt | St = s, At = a]
= E [Rt+1 + Rt+2 + Rt+3 + ... | St = s, At = a]
I We will talk in depth about state and action values later
26. Core concepts
The reinforcement learning formalism includes
I Environment (dynamics of the problem)
I Reward signal (specifies the goal)
I Agent, containing:
I Agent state
I Policy
I Value function estimate?
I Model?
I We will now go into the agent
29. Environment State
I The environment state is the
environment’s internal state
I It is usually invisible to the agent
I Even if it is visible, it may contain lots of
irrelevant information
30. Agent State
I The history is the full sequence of observations, actions, rewards
Ht = O0, A0, R1, O1, ..., Ot−1, At−1, Rt, Ot
I For instance, the sensorimotor stream of a robot
I This history is used to construct the agent state St
31. Fully Observable Environments
Full observability
Suppose the agent sees the full environment state
I observation = environment state
I The agent state could just be this observation:
St = Ot = environment state
32. Markov decision processes
Markov decision processes (MDPs) are a useful mathematical framework
Definition
A decision process is Markov if
p (r, s | St, At) = p (r, s | Ht, At)
I This means that the state contains all we need to know from the history
I Doesn’t mean it contains everything, just that adding more history doesn’t help
I =⇒ Once the state is known, the history may be thrown away
I The full environment + agent state is Markov (but large)
I The full history Ht is Markov (but keeps growing)
I Typically, the agent state St is some compression of Ht
I Note: we use St to denote the agent state, not the environment state
33. Partially Observable Environments
I Partial observability: The observations are not Markovian
I A robot with camera vision isn’t told its absolute location
I A poker playing agent only observes public cards
I Now using the observation as state would not be Markovian
I This is called a partially observable Markov decision process (POMDP)
I The environment state can still be Markov, but the agent does not know it
I We might still be able to construct a Markov agent state
34. Agent State
I The agent’s actions depend on its state
I The agent state is a function of the history
I For instance, St = Ot
I More generally:
St+1 = u(St, At, Rt+1, Ot+1)
where u is a ‘state update function‘
I The agent state is often much smaller than the
environment state
39. Agent State
These two states are not Markov
How could you construct a Markov agent state in this maze (for any reward signal)?
40. Partially Observable Environments
I To deal with partial observability, agent can construct suitable state representations
I Examples of agent states:
I Last observation: St = Ot (might not be enough)
I Complete history: St = Ht (might be too large)
I A generic update: St = u(St−1, At−1, Rt, Ot) (but how to pick/learn u?)
I Constructing a fully Markovian agent state is often not feasible
I More importantly, the state should allow good policies and value predictions
43. Policy
I A policy defines the agent’s behaviour
I It is a map from agent state to action
I Deterministic policy: A = π(S)
I Stochastic policy: π(A|S) = p (A|S)
46. Value Function
I The actual value function is the expected return
vπ (s) = E [Gt | St = s, π]
= E
Rt+1 + γRt+2 + γ2
Rt+3 + ... | St = s, π
I We introduced a discount factor γ ∈ [0, 1]
I Trades off importance of immediate vs long-term rewards
I The value depends on a policy
I Can be used to evaluate the desirability of states
I Can be used to select between actions
47. Value Functions
I The return has a recursive form Gt = Rt+1 + γGt+1
I Therefore, the value has as well
vπ (s) = E [Rt+1 + γGt+1 | St = s, At ∼ π(s)]
= E [Rt+1 + γvπ (St+1) | St = s, At ∼ π(s)]
Here a ∼ π(s) means a is chosen by policy π in state s (even if π is deterministic)
I This is known as a Bellman equation (Bellman 1957)
I A similar equation holds for the optimal (=highest possible) value:
v∗(s) = max
a
E [Rt+1 + γv∗(St+1) | St = s, At = a]
This does not depend on a policy
I We heavily exploit such equalities, and use them to create algorithms
48. Value Function approximations
I Agents often approximate value functions
I We will discuss algorithms to learn these efficiently
I With an accurate value function, we can behave optimally
I With suitable approximations, we can behave well, even in intractably big domains
51. Model
I A model predicts what the environment will do next
I E.g., P predicts the next state
P(s, a, s0
) ≈ p (St+1 = s0
| St = s, At = a)
I E.g., R predicts the next (immediate) reward
R(s, a) ≈ E [Rt+1 | St = s, At = a]
I A model does not immediately give us a good policy - we would still need to plan
I We could also consider stochastic (generative) models
55. Maze Example: Value Function
-14 -13 -12 -11 -10 -9
-16 -15 -12 -8
-16 -17 -6 -7
-18 -19 -5
-24 -20 -4 -3
-23 -22 -21 -22 -2 -1
Start
Goal
I Numbers represent value vπ (s) of each state s
56. Maze Example: Model
-1 -1 -1 -1 -1 -1
-1 -1 -1 -1
-1 -1 -1
-1
-1 -1
-1 -1
Start
Goal
I Grid layout represents partial transition model Pa
ss0
I Numbers represent immediate reward Ra
ss0 from each state s (same for all a and s0 in this
case)
58. Agent Categories
I Value Based
I No Policy (Implicit)
I Value Function
I Policy Based
I Policy
I No Value Function
I Actor Critic
I Policy
I Value Function
59. Agent Categories
I Model Free
I Policy and/or Value Function
I No Model
I Model Based
I Optionally Policy and/or Value Function
I Model
61. Prediction and Control
I Prediction: evaluate the future (for a given policy)
I Control: optimise the future (find the best policy)
I These can be strongly related:
π∗(s) = argmax
π
vπ (s)
I If we could predict everything do we need anything else?
62. Learning and Planning
Two fundamental problems in reinforcement learning
I Learning:
I The environment is initially unknown
I The agent interacts with the environment
I Planning:
I A model of the environment is given (or learnt)
I The agent plans in this model (without external interaction)
I a.k.a. reasoning, pondering, thought, search, planning
63. Learning Agent Components
I All components are functions
I Policies: π : S → A (or to probabilities over A)
I Value functions: v : S → R
I Models: m : S → S and/or r : S → R
I State update: u : S × O → S
I E.g., we can use neural networks, and use deep learning techniques to learn
I Take care: we do often violate assumptions from supervised learning (iid, stationarity)
I Deep learning is an important tool
I Deep reinforcement learning is a rich and active research field
65. Atari Example: Reinforcement Learning
observation
reward
action
at
rt
ot
I Rules of the game are unknown
I Learn directly from interactive
game-play
I Pick actions on joystick, see
pixels and scores
66. Gridworld Example: Prediction
3.3 8.8 4.4 5.3 1.5
1.5 3.0 2.3 1.9 0.5
0.1 0.7 0.7 0.4 -0.4
-1.0 -0.4 -0.4 -0.6 -1.2
-1.9 -1.3 -1.2 -1.4 -2.0
A B
A’
B’
+
10
+5
Actions
(a) (b)
Reward is −1 when bumping into a wall, γ = 0.9
What is the value function for the uniform random policy?
67. Gridworld Example: Control
a) gridworld b) V* c) *
22.0 24.4 22.0 19.4 17.5
19.8 22.0 19.8 17.8 16.0
17.8 19.8 17.8 16.0 14.4
16.0 17.8 16.0 14.4 13.0
14.4 16.0 14.4 13.0 11.7
A B
A’
B’
+
10
+5
π
What is the optimal value function over all possible policies?
What is the optimal policy?
68. Course
I In this course, we discuss how to learn by interaction
I The focus is on understanding core principles and learning algorithms
Topics include
I Exploration, in bandits and in sequential problems
I Markov decision processes, and planning by dynamic programming
I Model-free prediction and control (e.g., Q-learning)
I Policy-gradient methods
I Deep reinforcement learning
I Integrating learning and planning
I ...